Tehran Stock Exchange Index Research Articles

Impact of global oil and gold prices on the Iran stock market returns during the Covid-19 pandemic using the quantile regression approach

The main aim of this paper is to investigate the influence of global oil and gold prices on the Iran stock market during the Covid-19 pandemic. The approach used in this study is the quantile regression method and the effective parameters on the stock price index in the deciles of 0.1–0.9 were studied. The daily data from February 20, 2020 until January 30, 2021 were used in order to achieve the purposes aforementioned. The results showed that there is not a significant relationship between the global gold price and the stock price Iran stock exchange index. In addition, in all studied deciles, there is a negative and significant relationship between OPEC oil prices and the Tehran Stock Exchange index. Therefore, it is necessary for investors and policymakers to consider the impact of changes in these variables on stock returns.

Detection of Bubbles in Tehran Stock Exchange Using Log-Periodic Power-Low Singularity Model

One of the essential factors that lead to severe disruptions in financial markets is price bubbles and subsequent crashes. Numerous models for detecting bubbles have been developed, one of which (LPPLS) has lately attracted considerable interest. This study aims to utilize this model to detect price bubbles in Tehran Stock Exchange's index (TEDPIX). Confidence multi-scale indicators for this model are presented by fitting the LPPLS model to the data of the TSE index from 2009 through 2020. The bubble is detected when the number of fits that are in our filter conditions increases which means the growth of the indicator's value. By applying this method on TSE data two significant crashes in 2013 and 2020 are detected. The proposed technique can be useful for market participants to detect financial crashes and bubbles.

Interaction between Financial Cycles and Business Cycles in Iran: A Bayesian Approach

Investigation of effective factors in business cycles in Iran, from 2009:3 to 2018: 3, based on Bayesian approach with emphasis on the effects of financial cycles, is the major purpose of this paper. At first, using Hodrick-Prescott filter, we extract business and financial cycles, then we study the causal relationship between financial and business cycles, using Granger causality test. There is a two-way causal relationship among the variables (GDP, Tehran Stock Exchange index, land value (per square meter), current government and non-government credits, oil revenues and current government payments), except for the credits and business cycles. Based on Bayesian approach, we found that housing sector cycles has negative and current government payment cycles has positive effect on the business cycles, and both of them are significant. However, Tehran Stock Exchange index, current government and non-government facilities and oil revenues do not have a significant effect on the business cycles.

The Proposed Algorithm to Select Appropriate Features for Predicting Tehran Stock Exchange Index

The Proposed Algorithm to Select Appropriate Features for Predicting Tehran Stock Exchange Index

US Withdrawal from JCPOA and the Turmoil in Iran’s Economy

Many individuals and companies make their economic decisions based on political developments. This article examines an important political event,namely the withdrawal of the United States from JCPOA and its impact on the Iranian capital market. With the withdrawal of the United States fromthe JCPOA and the United States’ threats to resume sanctions, Iran’s economy had experienced turmoil and drastic changes, which clearly saw in thedays before and after the resumption of sanctions in Iran’s financial markets. In this study, we intend to examine the changes resulted from the USwithdrawal from JCPOA, its impact on Iran’s oil-dependent economy, financial markets, analyze the spread, and severity of turbulence in the Iranianeconomy, especially the Iranian financial markets using multivariate GARCH methods. In this regard, the multivariate GARCH model (DCC) andanalysis of variance are used to investigate these events’ effect on the Tehran Stock Exchange index.
 Keywords: ARCH, Economic Dependence, Economic Sanctions, Intensity of Turbulence in Iran’s Economy, Linear combinations of univariateGARCH models, Multivariate generalized autoregressive conditional heteroscedasticity, Nonlinear combinations of univariate GARCH models, USWithdrawal from JCPOA, Multivariate GARCH

Pattern recognition in financial surveillance with the ARMA-GARCH time series model using support vector machine

As the intersection of finance and statistics, financial surveillance is a new interdisciplinary field of research. In this field, statistical process control methods are applied to monitor financial indices. The final aim is to detect out-of-control conditions and trigger a signal as soon as possible. These early signals can help practitioners in making on-time decisions. In this paper, a new method based on a support vector machine is proposed to detect upward and downward shifts with step and trend patterns in auto-correlated financial processes. These processes are modeled by the autoregressive moving average (ARMA) and generalized autoregressive conditional heteroskedasticity (GARCH) variance (ARMA-GARCH) time series model. Autocorrelation structure in data with changing volatility makes pattern recognition difficult. As such, some features are selected to extract different properties of the considered patterns. Moreover, a new feature named the maximum degree between the horizontal line and the consecutive observations line is used for the better distinction of the step and trend shift patterns. To evaluate the performance of the proposed method, we performed a comprehensive simulation study. Moreover, to illustrate the proposed method's application, it was applied to classify patterns in the OPEC crude oil basket return and the Tehran stock exchange index.

Measurement of Communication and Systematic Risk in Tehran Stock Exchange Index (with Linear and Nonlinear Granger Causality Approach and Regression Switching)

Measurement of Communication and Systematic Risk in Tehran Stock Exchange Index (with Linear and Nonlinear Granger Causality Approach and Regression Switching)

Simulation the Model of Effects of Behavioral and Macroeconomic Factors on the Tehran Stock Exchange Index with Using System Dynamics Approach

پژوهش حاضر با هدف ارائه الگویی برای پیش­بینی تغییرات شاخص کل «بورس اوراق بهادار تهران» با در­نظر­گرفتن عوامل رفتاری و کلان اقتصادی حاکم بر بازارهای پولی و مالی کشور صورت گرفته است. اهمیت و ضرورت این مطالعه از آن رو است که بر خلاف سایر مطالعات پیشین به­منظور تحقق این مهم تنها به ابعاد کلان اقتصادی پرداخته نشده و علاوه­بر این بُعد، ابعاد رفتاری نیز بررسی شده است تا قدرت تبیین­کنندگی مدل را نسبت به مدل­های ارائه­شده در این راستا ارتقا دهد. این امر با استفاده از روش پویایی­شناسی سیستمی و ارتباط میان سوگیری­های رفتاری، دارایی­های رقیب سهام (قیمت طلا، نرخ ارز، قیمت مسکن و قیمت نفت) و عوامل کلان اقتصادی صورت گرفته است. نتایج پژوهش مؤید این موضوع است که متناسب با شرایط پیش رو تا سال 1400، با افزایش ارزش تمامی دارایی­های رقیب سهام، به­تبع شاخص کل «بورس اوراق بهادار تهران» نیز از این الگو پیروی خواهد کرد. در این راستا باید توجه داشت که رشد چند برابری ارزش هر یک از دارایی­های موجود در بازارهای پولی و مالی کشور در یک بازه زمانی کوتاه­مدت نشان­دهنده بهبود و رشد وضعیت اقتصادی کشور به­شمار نمی­رود؛ بلکه این امر به­واسطه جذب سرمایه­گذاران ناآگاه به این بازارها رخ داده است و می­تواند زمینه وقوع بحران­های اقتصادی را برای کشور به همراه داشته باشد.

The impact of oil price shocks on Tehran Stock Exchange returns: Application of the Markov switching vector autoregressive models

Global oil price fluctuations have a significant impact on various areas of the financial sector especially the stock market. This is evident in the oil-dominated Iranian economy. This study investigates the impact of world oil price shocks on the Tehran Stock Exchange using the Markov switching vector autoregressive (MS-VAR) model based on two regimes. Key variables for this study are world oil prices and the Tehran Stock Exchange Index. In addition, monthly time series data from 2002 to 2017 is used to estimate the MSIAH(2)-VAR(1) model and calculate the regime-dependent impulse response function of the impact of oil price shocks on the stock market under the different regimes. The findings show that the intercepts, coefficients, and variances of the model are different in both regimes and, based on the transition probability matrix, both the regimes are persistent. Thus, the shocks have both positive and negative impacts on the Tehran Stock Exchange in regimes one and two, respectively. This effect disappears earlier in regime two. Robustness tests employing daily data confirm the results of the monthly data. Nevertheless, regime-dependent analysis can provide a clearer understanding of Iran's economy to policymakers and investors.

Analyzing the Structural Breaks and the Exchange Market Turbulence on Volatility Spillovers Between Exchange Rates and Tehran Stock Exchange

This article has studied several fluctuations in the Iranian currency market and multiple turmoils in the economy that have not only wiped out Iranians private savings but also affected financial market activists to provide a better understanding of fluctuations' movement between markets. To doing so, we used the exchange rate in the open market (in some periods, the black market) as one variable and the Tehran Stock Exchange Index (TEDPIX) as a second in the form of multivariate conditional heterogeneity variance (MGarch) model. According to the results, the time series suggests multiple structural breaks from Dec. 2018 to Jan. 2020. Using the so-called GLS-Based unit root test, we observed five structural breaks that produced stationary problems at the level and no evidence of stationary problems at the return of the data. Also, by using DCC and FDCC models we confirm that there is a fluctuation between the two markets during the period. This overflow shows a different performance if structural failures are considered.

A Regulated Market Under Sanctions: On Tail Dependence Between Oil, Gold, and Tehran Stock Exchange Index

We demonstrate that the tail dependence should always be taken into account as a proxy for systematic risk of loss for investments. We provide the clear statistical evidence of that the structure of investment portfolios on a regulated market should be adjusted to the price of gold. Our finding suggests that the active bartering of oil for goods would prevent collapsing the national market facing international sanctions.

Comparative Study of Static and Dynamic Artificial Neural Network Models in Forecasting of Tehran Stock Exchange

During the recent decades, neural network models have been focused upon by researchers due to their more real performance and on this basis, different types of these models have been used in forecasting. Now, there is a question that which kind of these models has more explanatory power in forecasting the future processes of the stock. In line with this, the present paper made a comparison between static and dynamic neural network models in forecasting (uninvariable) the return of Tehran Stock Exchange (TSE) index in order to find the best model to be used for forecasting this series. The data were collected daily from 26/11/2009 to 17/10/2014. The models examined in this study included two static models (Adaptive Neuro-Fuzzy Inference Systems and Multi-layer Feed-forward Neural Network MFNN) and a dynamic model (nonlinear neural network autoregressive model NNAR). The findings showed that based on the Mean Square Error and Root Mean Square Error criteria, ANFIS model had a much higher forecasting ability compared to other models.

Comparison of several combined methods for forecasting Tehran stock exchange index

Comparison of several combined methods for forecasting Tehran stock exchange index

Comparison of several combined methods for forecasting Tehran stock exchange index

Forecasting economic and financial variables is of high interest to economic policy-makers in all countries. In this paper, the Tehran Stock Exchange Price Index (TEPIX) is estimated and forecasted using daily data for the period 22 May 2011 to 11 August 2011. To achieve that goal, various forecasting methods will be applied, including ARIMA, FARIMA, ANN and ANFIS models. Comparing the forecast accuracy of the models mentioned above, using forecast accuracy measures such as RMSE, MAE, MAPE and U-Thiel implied that the combined models of ANFIS and FARIMA have outperformed other models of forecasting daily stock indices. However, statistical comparison of forecast accuracy of different models using statistics such as Harvey, Leybourne and Newbold shows no significant difference between the forecast accuracy of these models.

Forecasting Tehran Stock Exchange index using the industry index and economic

Stock price index reflects the overall state of the country's economy. Increase in the index means prosperity and improvement in economic conditions and its decrease indicates downturn and crisis. Artificial neural network based on GMDH Shell algorithm, which results in predicting stock price index, can be very useful. In this paper, it is tried to identify the factors affecting the stock exchange index by modeling and predicting the Tehran Stock Exchange Index based on a combination of genetic algorithms with neural network approach. In this paper, the aim is to predict the Tehran Stock Exchange Index with a look at the different industries index and the variables affecting it, by applying neural network based on genetic algorithm. For this purpose, the trend of changes during 2010 to 2013 was considered monthly. First, the theoretical literature is explained. Research literature is then discussed. After that, the neural network is described. The results of modeling and forecasting the stock index are provided, and finally in the end, the discussions and conclusions are summarized.

The Study of the Relationship between Liquidity Growth and Return Market of Tehran Stock Exchange

The aim of the study was to examine the relationship between liquidity growth as one of the macroeconomic variables and the return of Tehran Stock Exchange index. This research method is descriptive-correlational. The study period included the years 1990 to the end of 2011. The population of the study was the stock index in the whole years of activity. Data for each of the variables was extracted from the site of the Central Bank, economic indicators, and stock indicators on a yearly basis for the period of the study and then was analyzed using SPSS by Pearson correlation test. The results of the study showed that a significant relationship did not exist between the liquidity growth and the index return of Tehran Stock Exchange.

Stock Market Index Prediction via Hybrid Inertia Factor PSO and Constriction Coefficient PSO

Conventional statistical techniques for forecasting are constrained by the underlying seasonality, non-stationary and other factors. Increasingly over the past decade, Artificial intelligence (AI) methods including Artificial Neural network (ANN), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) etc. have been used successfully to perform predictions in financial markets and other areas. This study presents a hybrid inertia factor and constriction coefficient PSO-based methodology to deal with the Stock market index problem. We will demonstrate the superiority an applicability of the proposed approach by using Tehran Stock Exchange Index (TSEI) data and comparing the outcomes with other PSO methods such as: standard PSO, Inertia Factor PSO and Constriction Coefficient PSO. Experimental results clearly show that a hybrid PSO approach meaningfully outperforms all of the other PSO methods in terms of MAD, MSE, RMSE and MAPE Evaluation statistics also, the proposed approach can be considered as a suitable AI model for stock market index forecasting problem.

A Study to Examine Time-Varying Effectiveness of Stock Returns on Tehran Stock Exchange

The present study aims to examine the presence of various anomalies or 'calendar effects' in stock index returns by using the Tehran Stock Exchange (TSE) index during the period 2004-2010. The findings show that the weekend effect, the weekend effect within the monthly effect, the monthly effect, and seasonal effect are present in the return equations of GARCH(1,1), GARCH-M, and Modified GARCH(1,1) models. Analyzes the weekend effect, and also the weekend effect in the first half and the second half of the month; this study finds a weekend effect: Wednesday's returns are significantly greater than Saturday's returns. However, the spread of returns on Wednesday and Saturday will be vary between the first half and the second half of the month. There are also monthly effects on excessive returns in Farvardin (March 20- April 19), Mordad (July 22- August 21) and Shahrivar (August 22- September 21), and on negative returns in Mehr (September 22- October 21) and Esfand (February 19- March 19) in the Tehran Stock Exchange. In terms of the seasonal effect, the study reveals that the highest rate of return can be achieved in the summer, while the lowest rate is appeared in the winter.